A New Hybrid Method to Detect Risk of Gastric Cancer using Machine Learning Techniques
- سال انتشار: 1402
- محل انتشار: مجله هوش مصنوعی و داده کاوی، دوره: 11، شماره: 4
- کد COI اختصاصی: JR_JADM-11-4_001
- زبان مقاله: انگلیسی
- تعداد مشاهده: 63
نویسندگان
Department of Computer Engineering, Rahbord Shomal Institute of Higher Education, Rasht, Iran.
Department of Computer Engineering, Rahbord Shomal Institute of Higher Education, Rasht, Iran.
Department of Computer Engineering, Faculty of Engineering, Lahijan Branch, Islamic Azad University, Lahijan, Iran.
چکیده
Machine learning (ML) is a popular tool in healthcare while it can help to analyze large amounts of patient data, such as medical records, predict diseases, and identify early signs of cancer. Gastric cancer starts in the cells lining the stomach and is known as the ۵th most common cancer worldwide. Therefore, predicting the survival of patients, checking their health status, and detecting their risk of gastric cancer in the early stages can be very beneficial. Surprisingly, with the help of machine learning methods, this can be possible without the need for any invasive methods which can be useful for both patients and physicians in making informed decisions. Accordingly, a new hybrid machine learning-based method for detecting the risk of gastric cancer is proposed in this paper. The proposed model is compared with traditional methods and based on the empirical results, not only the proposed method outperform existing methods with an accuracy of ۹۸% but also gastric cancer can be one of the most important consequences of H. pylori infection. Additionally, it can be concluded that lifestyle and dietary factors can heighten the risk of gastric cancer, especially among individuals who frequently consume fried foods and suffer from chronic atrophic gastritis and stomach ulcers. This risk is further exacerbated in individuals with limited fruit and vegetable intake and high salt consumption.کلیدواژه ها
Artificial intelligence, Machine learning, Gastric cancer, Hybrid method, Neural networkاطلاعات بیشتر در مورد COI
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